Journal
SCIENCE TRANSLATIONAL MEDICINE
Volume 14, Issue 628, Pages -Publisher
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/scitranslmed.abj1441
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Funding
- NIH [NBIB DP2-EB029757, NINDS UG3NS123723-01, NIDA R01-DA050159]
- NSF [1728497, 1351980, DGE-1650112]
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [1728497] Funding Source: National Science Foundation
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The study developed a reconfigurable thin-film multithousand-channel neurophysiological recording grid, which provides high spatial and temporal resolution. Experimental results demonstrate the effectiveness and practicality of the grid in both animal and human subjects.
Electrophysiological devices are critical for mapping eloquent and diseased brain regions and for therapeutic neuromodulation in clinical settings and are extensively used for research in brain-machine interfaces. However, the existing clinical and experimental devices are often limited in either spatial resolution or cortical coverage. Here, we developed scalable manufacturing processes with a dense electrical connection scheme to achieve reconfigurable thin-film, multithousand-channel neurophysiological recording grids using platinum nanorods (PtNRGrids). With PtNRGrids, we have achieved a multithousand-channel array of small (30 mu m) contacts with low impedance, providing high spatial and temporal resolution over a large cortical area. We demonstrated that PtNRGrids can resolve submillimeter functional organization of the barrel cortex in anesthetized rats that captured the tissue structure. In the clinical setting, PtNRGrids resolved fine, complex temporal dynamics from the cortical surface in an awake human patient performing grasping tasks. In addition, the PtNRGrids identified the spatial spread and dynamics of epileptic discharges in a patient undergoing epilepsy surgery at 1-mm spatial resolution, including activity induced by direct electrical stimulation. Collectively, these findings demonstrated the power of the PtNRGrids to transform clinical mapping and research with brain-machine interfaces.
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